Predicting the Risk of Injuries Through Assessments of Asymmetric Lower Limb Functional Performance: A Prospective Study of 415 Youth Taekwondo Athletes
Bibliographic record
Abstract
Background: The impact of interlimb asymmetries on sport injuries is unclear because of inconsistent findings, and there is a lack of research on youth athletes and the sport of taekwondo. Purpose: To examine the effects of functional interlimb asymmetries on noncontact lower limb injuries in youth athletes. Study Design: Cohort study; Level of evidence, 2. Methods: A total of 415 taekwondo athletes (318 boys and 97 girls) aged 6 to 17 years underwent baseline testing to determine interlimb asymmetries through the single-leg countermovement jump (CMJ), hop, and triple hop tests as well as the Star Excursion Balance Test. The athletes were then evaluated for 12 months to observe the occurrence of noncontact lower limb injuries. Results: During the study, 98 athletes (70 boys and 28 girls) sustained at least 1 noncontact lower limb injury. Athletes with higher interlimb asymmetries in single-leg CMJ height showed a significantly increased risk of noncontact lower limb injuries (boys: odds ratio [OR], 1.053 [95% CI, 1.027-1.080], P < .001; girls: OR, 1.070 [95% CI, 1.016-1.128], P = .011). Asymmetry in single-leg CMJ height of ≥15.28% was found to be the cutoff point for predicting noncontact lower limb injuries in boys (OR, 4.652 [95% CI, 2.577-8.398]; P < .001). Conclusion: This study highlights the utility of interlimb asymmetries in unilateral jump performance as a tool for assessing the risk of noncontact lower limb injuries in youth taekwondo athletes of both sexes. A proper evaluation of interlimb asymmetries may improve prevention strategies for youth athletes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".